NON-ASSOCIATIVE MARKOV NETWORKS FOR 3D POINT CLOUD CLASSIFICATION

By Roman Shapovalov, Alexander Velizhev, Olga Barinova, L. Gory. 2010

114 citations recorded by the source corpus.

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Citations in the corpus

DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS (2012, background): “The second dataset (ALSR) concerns a residential area in Russia, with 5 pts/m2 (Shapovalov et al., 2010).”
Contextual classification of lidar data and building object detection in urban areas (2014, background): “Shapovalov et al. (2010) show that using a segmented point cloud will lead to a loss of 1–3% in overall accuracy due to segmentation errors and due to the fact that classes having few samples such as cars might be merged with the background.”
Contextually Guided Semantic Labeling and Search for 3D Point Clouds (2011, background): “More importantly, many of the works discussed above (e.g., Shapovalov et al., 2010; Shapovalov and Velizhev, 2011; Munoz et al., 2009; Anguelov et al., 2005; Xiong et al., 2011) were designed for outdoor scenes with LIDAR data (without RGB values).”
Contextually guided semantic labeling and search for three-dimensional point clouds (2013, background): “More importantly, many of the works discussed above (e.g., Anguelov et al., 2005; Munoz et al., 2009; Shapovalov et al., 2010; Shapovalov and Velizhev, 2011; Xiong et al., 2011) were designed for outdoor scenes with LIDAR data (without RGB values).”
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY (2016, background): “For example, (Shapovalov et al., 2010) propose a non-associative Markov random ﬁeld for semantic 3D point cloud labelling, after a pre-segmentation of the point cloud into homogeneous 3D segments.”
SEGCloud: Semantic Segmentation of 3D Point Clouds (2017, unknown): “These models are shown to work well for this task [50, 49, 36, 58, 44, 3, 48].”
Semantic Labeling of 3D Point Clouds for Indoor Scenes (2011, background): “All of these works [1, 23, 29, 30, 36] were designed for outdoor scenes with LIDAR data (without RGB values) and therefore would not apply directly to RGB-D data in indoor environments.”